moabb.datasets.Nakanishi2015#

class moabb.datasets.Nakanishi2015[source]#

SSVEP Nakanishi 2015 dataset.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/nakanishi2015-moabb

Dataset summary

#Subj

#Chan

#Classes

#Trials / class

Trials length

Sampling rate

#Sessions

9

8

12

15

4.15s

256Hz

1

This dataset contains 12-class joint frequency-phase modulated steady-state visual evoked potentials (SSVEPs) acquired from 10 subjects used to estimate an online performance of brain-computer interface (BCI) in the reference study [1].

References

1

Masaki Nakanishi, Yijun Wang, Yu-Te Wang and Tzyy-Ping Jung, “A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol.10, no.10, e140703, 2015. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#

Get path to local copy of a subject data.

Parameters

  • subject (int) – Number of subject to use

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type

list of str